With the advent of Web 2.0 and the behavior change which it brought, there are millions of users worldwide contributing to different databases with various forms of data, such as movie ratings, for example. Moreover, the same real-world object (a song, a band or a movie) can be modeled using different ontologies or represented in different ways within the same ontology. Thus, the same film is often described by different attributes in different databases, making it difficult to perform an automatic mapping between those databases. We propose MovieMatcher, which is a heuristic that matches films across different databases using their metadata. After performing 2 experiments with the attempt to match 500 films to IMDb and Rotten Tomatoes databases, Movie- Matcher had a success rate of 97.4% and 94.1%, in contrast to an alternative, simpler approach (title exact matching), which had a success rate of 80.8% and 81.9%, respectively.

The widespread of social communication media on the Web has made available a large volume of opinionated textual data stored in digital format. These media constitute a rich source for sentiment analysis and understanding of the opinions spontaneously expressed. Traditional techniques for sentiment analysis are based on POS Tagger. Considering the Portuguese language, the use of POS Tagging ends up being too costly, due to the complex grammatical structure of this language. Faced with this problem, a case study is carried out in order to compare two techniques for sentiment analysis: a SVM versus Naive-Bayes classifiers. Our study focused on tweets written in Portuguese during the 2013 FIFA Confederations Cup, although our technique could be applied to any other language. The achieved results indicated that the SVM technique surpassed the Naive-Bayes one, concerning performance issues.

The purpose of this work is to understand the complex reality of eBay ecommerce network, their connections and the dynamics of its users. Data were collected using a script developed in this work, and it resulted in a database of approximately 87 million transactions and 15 million different dealer users. From these data, the characterization was made estimating network metrics, like dealer users' degree distribution, that gave us key insights about the eBay negotiation network. We found that there are users who bought/sold for more than 100.000 different persons. We also found that that a user A interacted over 4.000 times with another user B in just 3 months. Those and other interesting results, such as average distance and feedbacks ratings, were obtained, analyzed and discussed in this work.

Learning about Web Design can be difficult and time consuming, yet students often do not learn from their errors and struggle to understand some differences between document structure, styling, scripting and temporal synchronization. In this paper we present Ambulant Sketchbook, an easy-to-use Web playground designed to enable students to understand and learn from their errors. In particular, this application simplifies the process of learning how to write and debug Web documents by exploring aspects of immediate feedback, coding assistance, direct manipulation and playback control. We have deployed and used Ambulant Sketchbook in a course of Web Design Foundations over a 2-week span. Based on the positive feedback from a group of post-secondary students, we expect the functionalities and experiences discussed in this work can yield significant insights to be considered in the design of next generation authoring tools and in the process of teaching Web Media related disciplines.

This paper presents a system for detection and warning of falls for people with special care, using real-time evaluated data from accelerometer and magnetometer sensors of mobile devices with android operating system, using algorithms to detect patterns of falls, device position and voice recognition to determine a possible fall. We performed 240 tests in a young healthy user using the Samsung Galaxy S3 I9300 device strapped to his chest in order to ensure efficiency in detecting falls.

The amount of information available in the Internet does not allow performing manual content analysis to identify information of interest. Thus automated analyses are used to identify information of interest, and one increasingly important approach is the polarity analysis. Polarity analysis is the classification of a text document in positive, negative, and neutral, according to a certain topic. This classification of information is particularly useful in the finance domain, where news about a company can affect the performance of its stocks. Although most of the methods in financial domain consider that the whole document is associated with a particular entity, this is not always the case. In fact, it is common that authors cite several entities in a single document and these entities are cited with different polarity. Accordingly, the objective of this paper was to study strategies for polarity detection in financial documents with multiple entities. Specifically, we studied methods based on learning of multiple models, one for each observed entity, using SVM classifiers. We evaluated models based on the partition of documents into fragments according to the entities they cite. We used several heuristics to segment documents based on shallow and deep natural language processing (NLP). We found that entity-specific models created by partitioning the document collection into segments outperformed the strategy based on the use of entire documents. We also observed that more complex segmentation using anaphora resolution was not able to outperform a low-cost approach, based on simple string matching.

The use of social networks has shown great potential for information diffusion and formation of public opinion. One key problem that has attracted researchers interest is Topic-based Influence Maximization, that refers to finding a small set of users on a social network that have the ability to influence a substantial portion of users on a given topic. The proposed solutions, however, are not suitable for large-scale social networks and must incorporate mechanisms for determining social influence among users on each topic of interest. Consequently, for these approaches, it becomes difficult or even unfeasible to deal quickly and efficiently with constant changes in the structure of social networks. This problem is particularly relevant as the topics of interest of users and the social influence they exert on each other for every topic are considered together. In this work is proposed a scalable solution, that makes use of data mining over an information propagation log, in order to directly select the initial set of influential users on a particular topic without the need to incorporate a previous step for learning users social influence with regard to that topic. As an additional benefit, the targeted seed set also offers an approximation guarantee of the optimal solution. Finally, it is presented a design of experiments over a data set containing information propagation data from a real social network. As main results, we have found some evidences that the proposed solution maintains a trade-off between scalability and accuracy.

Annotation tools have already been applied in the educational context successfully for a few years, supporting teachers and students to mark relevant parts of a content and to associate additional information with this content. This paper presents DLNotes2, an e-learning tool that supports the creation of structured and semantic (ontology-based) annotations on HTML documents.

KW-GPS is a system to assist users intent on enjoying Web resources related to a domain-restricted collection of stories. In this system, each story is referenced in a virtual library in terms of the following data: (1) the URLs of resources associated with the story, which include but are not limited to plot-summaries, narrative texts, and videos; and (2) keywords of different classes, which serve as a multi-aspect index mechanism. Library items also include story templates, representing narrative motifs. Furthermore, a reduced version of the tool runs the basic rank-and-show process on mobile devices, such as tablets and cell phones.

Currently the use of location-based social networks are becoming quite popular. For example, Foursquare reported 50 million users in 2014. Data from this type of system can be viewed as a source of sensing, in which the sensors are users with their mobile de- vices sharing data on various aspects of the city. This source of data enables large-scale study of urban social behavior and city dy- namics. In this paper we show how we can use the signals emitted by Foursquare users to better understand the differences between the behavior of tourists and residents. We analyze tourists and res- idents in four popular cities around the world: London, New York, Rio de Janeiro and Tokyo. One of the contributions of this work is the spatio-temporal study of properties of the behavior of these two classes of users (tourists and residents). We have identified, for example, that some locations have features that are more correlated with the tourists’ behavior, and also that even in places frequented by tourists as well as residents there are clear differences in the patterns of behavior of these classes. Our results could be useful in several cases, for example, to help in the development of new recommendation systems specific for tourists.

Twitter has hundreds of millions users sending messages (tweets) and expressing their opinions about a myriad of subjects, for instance, what they think about a certain pro- duct, or if they liked or not a movie, or their reactions during a soccer game. This massive amount of messages carrying opinions about different things could be valuable for business and institutions. Because, it is possible to monitor many people in real time to obtain what they are expressing in their messages in an automatic, fast, and authentic way. In this work, I present Emotte, a tool to analyze sentiment in messages sent to Twitter using machine learning and natural language processing algorithms. The tool monitors the tweets according to queries, classify their sentiments, and display the results using a chart. Thus, someone using Emotte can compare over time the evolution of the opinion of the Twitter users about certain subjects.

In this paper, we propose a technique to automatically describe items based on users' reviews in order to be used by recommender systems. For that, we extract items' features using a robust term extraction method that applies transductive semi-supervised learning to automatically identify aspects that represent the different subjects of the reviews. Then, we apply sentiment analysis in a sentence level to indicate the polarities, yielding a consensus of users regarding the features of items. Our approach is evaluated using a collaborative filtering method, and comparisons using structured metadata as baselines show promising results.

The exponential growth in the use of web services and applications has increased the amount of personal information registered on websites and databases worldwide. Consequently, users are more exposed to vulnerability flaws and more subject to the impact of leaking this information. This article investigates how personal information is handled by three popular browsers: Internet Explorer, Mozilla Firefox and Google Chrome, by analyzing data collected in shared research laboratories. Through the analysis of data stored by browsers on a shared computer laboratory, we found a large number of cookies with important personal information, showing that these browsers can reveal more than the users would like.

How does my body impact my digital experience? How does my digital experience impact my body? In the performance piece “Voiced/Unvoiced,” I use interactive technology and custom software to immerse my live body in a digital world. Sheathed in white fabric, I move through a field of projected text. Gradually, my live body disappears, giving way to a digitally projected body. I attempt to both embrace and escape this digital other. My conflicting impulses reflect the continually shifting authority between the digital and the physical.

"A small army of gadgets are fighting for dominance in your living room and as your personal, portable do-it-all device. These gadgets come with lots of cool services, however many of these devices are difficult to use. The key to the future of these devices is not increasing processing power but how will we interact with this increasingly complex technology. Human-Computer Interaction (HCI) is fundamental to making products more successful, safe, useful, functional and, in the long run, more pleasurable for the user.

This talk will introduce a number of novel emerging technologies and discuss their importance. There are a number of problems inherent in the shift in society to an increasing reliance on technology and a number of facets of this trend need to be examined. At first glance, many new innovations may be seen as potentially useful in many situations, and they are often treated like any previous technology regarding their use and acceptance. However, perhaps we need to take special care and attention due to the inherently pervasive nature of many new technologies, and the undue reliance that the user may place upon them.

This keynote presentation will also showcase some of the latest developments and new technologies and demonstrate a range of projects underway here at SUNY Oswego. The talk will begin by showing Dr. Schofield’s work applying computer games technology to forensic reconstruction and the results of his research in this field. A number of recent projects from SUNY Oswego will then be showing including augmented reality educational tools, robot theatre, using game technology to learn to play music instruments, virtual art galleries, drone based research and finishing with a demonstration of international, collaboratively produced, films starring robot actors."

The volume of electronic transactions has raised a lot in last years, mainly due to the popularization of e-commerce. We also observe a significant increase in the number of fraud cases, resulting in billions of dollars losses each year worldwide. Therefore, it is essential to develop and apply techniques that can assist in fraud detection. In this direction, we propose an evolutionary algorithm to automatically build Bayesian Network Classifiers (BNCs) tailored to solve the problem of detecting fraudulent transactions. BNCs are powerful classification models that can deal well with data features, missing data and uncertainty. In order to evaluate the techniques, we adopt an economic efficiency metric and apply them to our real dataset. Our results show good performance in fraud detection, presenting gains up to 17%, compared to the actual scenario of the company.

The use of mobile devices brings great benefits of connectivity to
its users. However, access to information through these devices is
a new challenge of interaction for users who have some kind of
disability. Currently, most mobile applications have accessibility
barriers that make it difficult or impossible the usage for many
individuals with special needs. To ensure access to the content to
all users regardless of their health status, this paper proposes a
model for evaluating user interfaces accessibility focused on
mobile devices. The proposed model takes into account the users
experience without neglecting the specificities of mobile context
and accessibility scenario. As partial results, there is a group of
perceptions observed from tests at evaluating a mobile application
developed for the deaf. Results show the challenges and new
perspectives to evaluate the mobile accessibility, since few
methods consider these two contexts